multiprocessing

问题 I'm running Jupyter 5.0.0 notebook with Python 3.5.3 on Windows 10. The following example code fails to run: from concurrent.futures import as_completed, ProcessPoolExecutor import time import numpy as np def do_work(idx1, idx2): time.sleep(0.2) return np.mean([idx1, idx2]) with ProcessPoolExecutor(max_workers=4) as executor: futures = set() for idx in range(32): future = winprocess.submit( executor, do_work, idx, idx * 2 ) futures.add(future) for future in as_completed(futures): print(future

问题 I'm running Jupyter 5.0.0 notebook with Python 3.5.3 on Windows 10. The following example code fails to run: from concurrent.futures import as_completed, ProcessPoolExecutor import time import numpy as np def do_work(idx1, idx2): time.sleep(0.2) return np.mean([idx1, idx2]) with ProcessPoolExecutor(max_workers=4) as executor: futures = set() for idx in range(32): future = winprocess.submit( executor, do_work, idx, idx * 2 ) futures.add(future) for future in as_completed(futures): print(future

问题 import multiprocessing as mp import time def build(q): print 'I build things' time.sleep(10) #return 42 q.put(42) def run(q): num = q.get() print num if num == 42: print 'I run after build is done' return else: raise Exception("I don't know build..I guess") def get_number(q): q.put(3) if __name__ == '__main__': queue = mp.Queue() run_p = mp.Process(name='run process', target=run, args=(queue,)) build_p = mp.Process(name='build process', target=build, args=(queue,)) s3 = mp.Process(name='s3',

问题 I tried to combine blocking tasks and non-blocking (I/O bound) tasks using ProcessPoolExecutor and found it's behavior pretty unexpected. class BlockingQueueListener(BaseBlockingListener): def run(self): # Continioulsy listening a queue blocking_listen() class NonBlockingListener(BaseNonBlocking): def non_blocking_listen(self): while True: await self.get_message() def run(blocking): blocking.run() if __name__ == "__main__": loop = asyncio.get_event_loop() executor = ProcessPoolExecutor()

问题 I ran below simple Python program - to do 4 processes separately. I expect the program completes execution in 4 seconds (as you can see in the code), but it takes 10 seconds - meaning it does not do parallel processing. I have more than 1 core in my CPU, but the program seems using just one. Please guide me how can I achieve parallel processing here? Thanks. import multiprocessing import time from datetime import datetime def foo(i): print(datetime.now()) time.sleep(i) print(datetime.now())

问题 I have an unittest which is using multiprocessing. After upgrading from Python 3.2 to Python 3.4 I get following error. I can't find a hint, what was changed inside Python and what I have to change, to make my code running. Thanks in advance. Traceback (most recent call last): File "<string>", line 1, in <module> File "C:\Python341_64\lib\multiprocessing\spawn.py", line 106, in spawn_main exitcode = _main(fd) File "C:\Python341_64\lib\multiprocessing\spawn.py", line 116, in _main self =

问题 I was playing with tflite and observed on my multicore CPU that it is not heavily stressed during inference time. I eliminated the IO bottleneck by creating random input data with numpy beforehand (random matrices resembling images) but then tflite still doesn't utilze the full potential of the CPU. The documentation mentions the possibility to tweak the number of used threads. However I was not able to find out how to do that in the Python API. But since I have seen people using multiple

问题 I'm a go-newbie, so please be gentle. So I've been using mutexes in some of my code for a couple weeks now. I understand the concept behind it: lock access to a certain resource, interact with it (read or write), and then unlock it for others again. The mutex code I use is mostly copy-paste-adjust. The code runs, but I'm still trying to wrap my head around it's internal working. Until now I've always used a mutex within a struct to lock the struct. Today I found this example though, which

问题 I am struggling to understand the difference between run() and start() . According to the documentation, run() method invokes the callable object passed to the object's constructor, while start() method starts the process and can be called only once. I tried an example below: def get_process_id(process_name): print process_name, os.getpid() p1 = multiprocessing.Process(target=get_process_id, args=('process_1',)) p2 = multiprocessing.Process(target=get_process_id, args=('process_2',)) p1.run()

问题 I am trying to create an architecture that will have a main parent process & it can create new child processes. The main parent process will always be on loop to check if there is any child process available. I have used ThreadedConnectionPool of psycopg2.pool module in order to have a common database connection for all child processes created. That means the program will be connecting once to the database and execute all the SQL queries for each of the child processes. So there is no need to